Everything I Learned About AI Agents in 2024 in 19 Minutes

Imagine having a team of workers that never sleeps, handles repetitive tasks with precision, and costs a fraction of hiring a human. Welcome to the world of AI agents. This year I discovered their potential and it changed everything for me. If you guys don't know me, my name's Nate. I've been working in automation for a while now, but when I started learning about AI agents earlier this year, something clicked and I knew I had to throw myself at it completely. So I quit my full-time job to throw myself entirely at mastering and building AI agents because I saw how these tools could cut costs, transform workflows, and unlock potential in ways that traditional automation never could. In this video, I'm gonna unpack everything that I've learned about building AI agents over the past year into the next 19 minutes. We'll cover the foundations, challenges I faced, and the secrets to building scalable systems that actually work. I'm gonna move pretty fast through these concepts, so let me know in the comments if I touch on anything that you'd like to see a more detailed video on. So stick around, because by the end of this video, you'll have the tools and insights to start building your own AI agents today. All the AI agents you see right here, I built out in N8N, so if you're interested in N8N and building agents, check out my channel because I've got lots of resources there. Real quick, I just wanted to say thank you guys so much for 10,000 subs. It makes me so happy to see that we were able to hit this milestone within just under 3 months. So I'm super, super appreciative for each and every one of you. Because of this, we're doing a $100 giveaway. The winner will be announced January 1st, and all you have to do to enter is join the FreeSchool community and then comment down below your school username. So let's not waste any time and hop into the key components of AI agents. Real quick, I just wanted to break down the difference between an assistant and an agent. An AI assistant is a reactive system that performs tasks through direct user interaction, like setting reminders or answering questions. But an AI agent operates autonomously. So the keyword here is autonomy. They're able to proactively manage complex, multi-step processes and make decisions without constant input. So like I said, the key difference lies in autonomy and complexity. Assistants require direction, whereas agents strategize and act independently. So breaking down the core components of AI agents, first up, we have the core agent. Think of this as the brain of the operation. The core agent is the central processing unit that integrates all functionalities. Every decision, every action, it all stems from the core. Without it, the AI agent is just a bunch of disconnected tools and data. Next, we have memory. This is really a game changer because the memory is what allows the agent to store and retrieve information so that it can maintain context and continuity over time, which means that the agent doesn't have to start from scratch every time it wants to tackle a new task. So it's like Imagine you have an assistant that can remember every single detail of your last conversation and previous conversations. So that's what the memory is gonna do. Then we have tools. These are the external resources or APIs that the agent is gonna use in order to perform specific tasks and take action. Whether it's like sending emails or managing your calendar, pulling data from a database, tools are what give the agent the arms and legs. The more tools that you give your agent, the more versatile it becomes, but it's all about finding that balance. Finally, and probably most importantly, we have the prompt. This is where the magic of problem solving really happens. The prompt helps the agent analyze problems, devise a strategy, and ultimately determine the steps that the agent needs to take to get the job done. It's what makes the agent more than just reactive. It makes the agent proactive, tackling tasks with a clear plan in mind. So now that we understand the core components, let's talk about the capabilities of AI agents. The first one is advanced problem solving. The agent can analyze situations, plan tasks, and then execute on those plans. Whether it's generating a detailed project report, writing functional code, or creating summaries of vast data, agents are able to handle repetitive tasks that would take humans hours to complete. Next, we have self-reflection and improvement. This is one of the most impressive capabilities of AI agents. They can analyze their own output, identify problems, propose solutions, and even communicate with other agents, which is super cool. This ability to improve through iteration sets them apart from static automation systems. Then we have tool utilization. So obviously tools are what give the agents the power to act, and they use them in a lot smarter ways than you may originally think. Once you hook up different tools to an agent and define what each tool does, they're able to decide which ones they want to use and in what order. Obviously, as the use case gets more complex, you're going to have to, you know, work in some more structured prompting. But these agents are genuinely pretty good at understanding how to conduct themselves when they're given a role and an incoming query. Finally, we have collaborative multi-agent frameworks. Like I touched on earlier, agents can speak to each other, which is super cool. One might handle planning, one might handle critiques, one might offer feedback, and this iterative loop where agents can collaborate and talk to each other dramatically enhances their performance. All right, so moving on to something that I've realized over the past months is so, so important: the foundation, which is data and context. We've all seen what they're capable of, but we need to talk about the fuel that powers these capabilities, which is data and context. Without high-quality data, up-to-date data, even the most advanced AI agents, it's like having a car without gas. So data is key. We've all heard the phrase data is the new oil, and when it comes to AI agents, it couldn't be more true. An agent is only going to be as good as the data that it has access to. If the data is outdated or incomplete, the agent will make poor decisions and ultimately give you inaccurate results. It's not just about the data though. The agent also needs context. Context gives the data meaning. Without context, even accurate data will lead to poor results. Agents need to understand the situation that they're working in in order to make the right decisions for you. The question then becomes, how do we give agents the data they need while also maintaining context? This is where vector databases come into play. These tools allow us to store and retrieve data in a way that's both fast and contextually aware. Systems like Pinecone, for example, let agents search vast amounts of information and find exactly what's relevant to to their task. A vector database basically stores information in a way that captures meaning and context of data and not just exact matches. It represents data as numerical vectors, which allows the AI agent to quickly search through and retrieve the most relevant data based on similarity, even if the exact words or phrases don't match, sort of like when you do a Google search. And so diving into the agent space, you're going to hear a lot about vector databases, and you're going to also hear a lot about RAG, which is Retrieval Augmented Generation, and as you can see based on this image, it's just the process of the agent going out to retrieve information and then getting it back and generating an answer. As I alluded to earlier, another key factor here is keeping the data up to date. Business data changes constantly. New leads come in, deals close, projects evolve. If your agent isn't working with the latest information, its performance is going to suffer. So how do we have the right mindset when we're approaching building out an AI agent or an agent framework? When I approach building an AI agent, I don't jump straight into N8N, which is what I typically use. I don't jump straight in there and start dragging around nodes and connecting things and writing prompts. The first step before you build anything is setting up the data foundation. This means asking yourself, how can I most optimally take all this information that I have over here and organize it and structure it in a way that the agent can accurately retrieve and use? So step 1 is building a robust data framework. It's going to be a big, big chunk of the process, getting all your information into a database like a vector database in a way that's clean, organized, and optimized for the agent to use efficiently. This is the foundation. If the agent doesn't have access to accurate, well-structured data, like we talked about, it's not gonna perform well. Then another huge chunk is roadmapping your goals. Once your data foundation's in place, you need to understand what are you trying to accomplish? What specific tasks do you want the agent to handle? By breaking down your objectives into smaller, actionable tasks, you can build out an agent framework with clarity and purpose. What we're trying to avoid here is building out an agent that initially works, but then 3 months later, having to rebuild that agent. Then we move into the build phase, which won't take as long if you have all your data in place and you have goals. So this is where you can actually get into N8N, start dragging and dropping tools around, plugging in workflows, assigning tools, and testing your agent's functionality. But even here, testing and iteration is key, which moves into the fourth step, testing and refining. This is the final piece of the puzzle. It isn't just about seeing if your agent works. It's about exposing it to different scenarios, identifying gaps in your logic, because trust me, there will be gaps. And then testing helps you uncover these edge cases to refine your database and your workflows further. So by starting with a strong foundation, you set yourself up for long-term success. A robust framework not only makes the initial build smoother but also makes it easier to update and scale your agent over time. This mindset is going to help you prioritize building up a good foundation, which is what separates a functional agent from one that's constantly breaking or requiring fixes down the line. This leads great into the point of architecture. And why architecture matters. It matters because we want to build out agents that are scalable and that we won't have to rebuild later. By architecting these agents with the goal of continuing to add on top of them, we can create some really cool systems that don't require the infrastructure to be reset if we want to make changes later on. So here are the things that you need to think about. First thing is to think about your inputs and your outputs. When you're planning out the AI agent, you need to think about what is the agent going to receive, and then what do we want the agent to give us? This simple step is really important in understanding the tasks that the agent needs to perform. So let's say you're building some sort of email agent. The input is reading an email and the output is responding to an email. Some tasks within that process between the input and output are going to happen every time, but others are going to be situational. Like, let's say we need to scrape availability, or let's say we need to generate some sort of quote, or let's say we need to check a knowledge base. Those things are going to be situational based on the input. So thinking through these scenarios will help you decide what tools the agent should have access to and which ones should be baked into the logic of the workflow. That are going to happen every time. For example, if you want every single conversation to be stored in some sort of database, you wouldn't want to give the agent a tool that can do that. If it happens every time, you want to bake it into the logic of the output or following the output. So breaking things down, it's super important to do this for every project, every workflow within the project, and every task within every workflow so that we can have these tasks that we can connect like Legos. Each Lego has a clearly defined job, and together they they perform a complete system. This modular approach ensures that your agents are focused, reliable, and easy to update. The best approach here is to create job function-based agents. Each agent specializes in a particular workflow like email management or scheduling or lead qualification. The specialization makes them more efficient and easier to debug. Ultimately, this is going to make your systems more modular, and modular design is key to scaling your systems. Like we talked about, breaking your agents down into smaller independent modules, you can easily remove or add or insert or update specific functions without having to redo the entire infrastructure because you can just sort of plug and play these things in. And then when you have all these different agents that have different specific functions, you're able to connect them to different tools. So let's say you build an agent workflow that sends emails. You can now have this tool be called by multiple other workflows whenever you need that function of sending an email. Okay, so now this sort of relates back to thinking about your inputs and your outputs, sequential versus parent chaining. On the left, we have the sequential, and on the right, we have parent chaining. So what is sequential chaining? It's exactly what it sounds like. One agent performs its task, passes the output directly to a next agent, which takes that input from the agent and then performs a task, and then passes the output to another agent. This linear approach is straightforward, and it works well for processes where each step depends on the previous one. The biggest advantage here is its simplicity. It's easy to set up and debug because each step follows logically from the last. However, it can become a bottleneck if one agent takes too long to complete its task or just fails entirely. Then we have parent chaining, which involves a central parent agent that coordinates multiple child agents. Instead of a linear flow, the parent agent evaluates the situation and then it's able to delegate tasks to different child agents appropriately. The parent collects their outputs and then integrates them into one cohesive result, which is the output. This system is going to be a lot more flexible and resilient compared to sequential chaining, because if one child fails, the The parent can reroute the task to another agent or have the child agent try again and handle the error more gracefully. However, it does require more planning and a little bit more complexity in terms of the architecture. So how do you choose between these two systems? It really depends on the workflow. Sequential training is best for straightforward processes with a clear order of tasks, and parent training is ideal for complex or more dynamic scenarios where multiple tasks can run in parallel and where some of the tools are situational. Understanding these methods is crucial for designing effective AI agent workflows Whether you go with sequential or parent or even like a hybrid approach, choosing the right method will determine how well your system performs. All right, so now moving on to, like I said, what is probably the most critical skill for making agents that work as expected, which is prompt engineering. The quality of your prompts determines the quality of your agent's output. A well-crafted prompt guides the agent's reasoning, ensures it understands the tasks, and reduces errors. So it's super, super important. So there are a few essential components to every strong prompt. And breaking these down will help you engineer them effectively. Starting off with the objective, you need to define the agent's overall goal. This gives the agent a clear sense of purpose. Then we have context. This provides the agent with all of the relevant background information, and it helps it understand the environment that it's working in. Obviously, we have tools. You want to outline each tool the agent can access, what each tool does, and when to use each one. Then we have instructions. You want to be explicit about what you want the agent to do and how you want it to take action. Output requirements specify exactly what the output should look like. And finally, we have examples. These are really the linchpin of a good prompt. They show the agent what a successful flow and what a successful output looks like, reducing ambiguity. If you want a deeper dive on prompting for AI agents specifically, check out this video that I'll tag right here where I sort of dive into this topic. Obviously, this process of prompt engineering is not a one and done. You're going to need to test, refine, and retest your prompts to get consistent results. Start simple, observe where the agent struggles, and adjust accordingly. Like I mentioned earlier, exposing the agent to different scenarios to see where it has gaps is an essential part of making sure that your agents can be trusted, and testing and refining the prompts is a huge chunk of this. Now I want to talk about challenges that I face when I'm building AI agents. It's a very fun and exciting process, but trust me, it's not without its challenges because you're going to run into a lot of issues and there's going to be a lot of headaches. So let's dive into some common hurdles that I face and how you, they can derail your process, but also what you can do to overcome them. So the first thing is data quality. It's easy to overlook how your data's being prepared and fed into the database, especially when everything is initially working because you think you're good to go. But trust me, you want to get it right the first time. I've gotten to the end of a build only to realize that the information in my vector database was unoptimized, missing metadata, and even had some nonsensical vectors. So I knew this would only cause the agent to get confused as we continued to feed in more data. So it forced me to go back and fix the entire data pipeline. The takeaway here is to pay close attention to how your data is being chunked, what the metadata looks like, and how it's stored in your vector database. Try to automate the data ingestion process to ensure everything is consistent, and always test the database early and often during the build to catch issues before they snowball. If you want to understand better how your data is being chunked and split and ultimately fed into a vector database, then go ahead and watch this video. I'll tag it right up here where I sort of show a lot of examples of how that works. Next, we have poor planning. Another challenge I faced a ton is building a system that works initially, but ultimately it's not scalable. You know, early on I start building an agent with a specific workflow in mind. It works well, but then when I try to add new features, I realized that the architecture couldn't handle it, so I ended up having to start over from scratch. The takeaway here is planning out your builds is crucial. Before you jump into n8n or any other agent building platform, you want to map out your goals, your workflows, your tasks that your agent needs to handle, not just now but in the future, because you're probably going to end up adding on more capabilities. Like we talked about, you want to adopt a modular design, breaking tasks into reusable components or tools that multiple agents will be able to call on later. This not only makes your builds more scalable but also saves you time when you need to pivot or add features later on. Third, we have the balance between simplicity and flexibility. There's the challenge of balancing these things because if your workflows are too rigid, adding new features becomes difficult, but but if they're too overly complex, it can be harder to manage and debug. You wanna start simple and focus on making workflows modular. As your agent capabilities grow, you can layer complexity without breaking the system. Finally, just wanted to talk about the mindset of having realistic expectations. Agents are not perfect and they will break and they will fail, but whether that's due to something you did or changes in the data or, um, shifts in the platform and the tools that they rely on, breakdowns are inevitable and it's okay. So the takeaway here is that you can't expect perfection, but you want to prepare yourself for troubleshooting and learning from those failures. Because every time an agent breaks, you're gaining insights into how it operates and how to make it better. So adopt a mindset of curiosity, adaptability, view every issue as an opportunity to understand your agent better and refine it. Over time, you're going to develop a troubleshooting process that not only solves your problems, but makes your future builds stronger. Okay, so the future of AI agents, starting to wrap up here, but let's zoom out and think about the bigger picture. What does the future hold for AI agents, and why should you start building them and understanding them today? This isn't just a passing trend. This is the future of work and automation. As these systems become more advanced, they're poised to replace repetitive, low-value tasks, freeing up humans to focus on creativity, strategy, and innovation. In the past, businesses scaled by hiring more people. In the future, scaling will mean building smarter AI agents to handle complex workflows with minimal human input. And so there's some concern around this, right? But the future isn't about AI replacing humans, it's about how can humans and AI work together? How can humans leverage AI to act as teammates, not just tools, collaborating with us to achieve goals faster and more efficiently? Why is right now the time to start? Because the technology is still evolving. We're still pretty early in this paradigm shift, but that's exactly why right now you want to get involved. By starting early, you can sort of position yourself as a leader in the space, gaining experience and insights that you need to stay ahead of the curve. So here are a few trends that I think we need to keep our eyes on within the next few years. The first one is increased autonomy. Agents are obviously going to become more self-reliant and smarter, and there will be a day when agents can build other agents, which will, will be pretty cool, but also maybe a little scary. Anyways, number 2, enhanced collaboration. Multi-agent systems will become more common with agents working together to handle these more complex workflows. We're going to have broader accessibility. So no-code and low-code platforms are already super accessible, but these will be even better for more, you know, non-developers, non-technical users. And then finally, more integration into everyday tools. So this is already happening, but agents are going to be embedded more often in the tools we use every day, like our CRMs, our email platforms, and project management systems, stuff like that. It's just going to become super, super common. So the takeaway here is that AI agents are not just the future, they're the present. So the sooner you start learning, experimenting, and building, the better you're positioning yourself to take advantage of this paradigm shift in the future. So if you haven't joined my free school community for the giveaway already, I would encourage you to do so. It's a great community. You can get your questions answered. It's a committed group of people that are, you know, dedicated towards building out AI agents and learning how they can help businesses. And also any videos that I make in the future, all the resources will be on there for free. Then I also have a paid community that you can check out in the description if you want to take your skills a little bit further, if you want access to 5 weekly calls real product insights and stuff like that. Then my last call to action is if you're looking to have this sort of stuff built out for you or for your business, or you're looking for AI consultancy services, then book a call using the link to my website in the description. But that's all. Really appreciate you guys making it to the end. Really appreciate all the support so far, and I'll see you guys in the next one. Thanks.